Video cameras are commonly used in various industry segments for security, surveillance and tracking purposes. Typically, these video cameras are positioned to capture streams of video frames over substantially all accessible space which is being monitored.
Image and video analytics can be utilized to extract meaningful insights from these captured streams of video frames. Insights derived by the image and video analytics can be used to make decisions and predictions in a variety of different types of applications. One such application is in the retail industry for tracking customer movements in shopping malls.
In shopping malls, there is a need to be able to monitor and track customer response to in-store displays and promotions. Currently, retailers are trying to use a variety of different types of analytic tools to understand buying patterns of customers. By way of example, many retailers analyze data obtained from customer loyalty cards and other programs to determine which stores are being most affected by customers who make inconsistent purchases, jump at coupons, or frequently return products. The data gathered by these analytics help retailers tailor future sales, coupons, and other promotions. Unfortunately, current analytic tools in the retail space are either expensive and/or ineffective.
For example, some existing analytic tools, utilize hardware based solutions, such as Motes, WSN, or RFID, to track customer movement and shelf inventory. In these examples, wireless transmitters are embedded in shopping carts and in overhead sensors positioned throughout the store, capturing reams of new data about how shoppers move through stores, where they stop to linger or compare prices, and how much time they spent in front a display. Unfortunately, these tracking analytics are very expensive to install and maintain.